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> ## Demogrpahic Summary Statistics of Survey Respondents
> ##
> ## Kevin Quinn
> ## 10/5/2021
> ##
> 
> mydata <- read.csv("../ScaleRaceSpring2017clean.csv")
> 
> 
> mydata <- mydata[, c(442:444, 468)]
> mydata$Age <- 2017 - mydata$R.age_1
> 
> mydata$Age.15.19 <- mydata$Age >= 15 & mydata$Age <= 19
> mydata$Age.20.24 <- mydata$Age >= 20 & mydata$Age <= 24
> mydata$Age.25.34 <- mydata$Age >= 25 & mydata$Age <= 34
> mydata$Age.35.44 <- mydata$Age >= 35 & mydata$Age <= 44
> mydata$Age.45.54 <- mydata$Age >= 45 & mydata$Age <= 54
> mydata$Age.55.64 <- mydata$Age >= 55 & mydata$Age <= 64
> mydata$Age.65.74 <- mydata$Age >= 65 & mydata$Age <= 74
> mydata$Age.75.84 <- mydata$Age >= 75 & mydata$Age <= 84
> mydata$Age.85plus <- mydata$Age >= 85
> 
> 
> 
> mydata <- mydata[!is.na(mydata$Race), ]
> mydata.black <- mydata[mydata$Race == "Black", ]
> mydata.white <- mydata[mydata$Race == "White", ]
> 
> 
> 
> ## age among black respondents
> category <- NULL
> age.cat.n <- NULL
> age.cat.pct <- NULL
> for (v in 6:14){
+     category <- c(category, colnames(mydata.black)[v])
+     age.cat.n <- c(age.cat.n, sum(mydata.black[,v], na.rm=TRUE))
+     age.cat.pct <- round(c(age.cat.pct,
+                      100 * (sum(mydata.black[,v], na.rm=TRUE) /
+                             nrow(mydata.black))), 1) 
+ }
> ## missing
> category <- c(category, "Missing")
> age.cat.n <- c(age.cat.n, sum(is.na(mydata.black[,v])))
> age.cat.pct <- round(c(age.cat.pct,
+                        100 * (sum(is.na(mydata.black[,v])) /
+                               nrow(mydata.black))), 1) 
> 
> black.age.table <- data.frame(Age.Category=category,
+                               N=age.cat.n, Pct=age.cat.pct)
> 
> 
> 
> 
> 
> ## age among white respondents
> category <- NULL
> age.cat.n <- NULL
> age.cat.pct <- NULL
> for (v in 6:14){
+     category <- c(category, colnames(mydata.white)[v])
+     age.cat.n <- c(age.cat.n, sum(mydata.white[,v], na.rm=TRUE))
+     age.cat.pct <- round(c(age.cat.pct,
+                      100 * (sum(mydata.white[,v], na.rm=TRUE) /
+                             nrow(mydata.white))), 1) 
+ }
> ## missing
> category <- c(category, "Missing")
> age.cat.n <- c(age.cat.n, sum(is.na(mydata.white[,v])))
> age.cat.pct <- round(c(age.cat.pct,
+                        100 * (sum(is.na(mydata.white[,v])) /
+                               nrow(mydata.white))), 1) 
> 
> white.age.table <- data.frame(Age.Category=category,
+                               N=age.cat.n, Pct=age.cat.pct)
> 
> 
> 
> 
> 
> 
> 
> 
> 
> 
> ## gender among black respondents
> category <- NULL
> gender.cat.n <- NULL
> gender.cat.pct <- NULL
> category <- c(category, "Female")
> gender.cat.n <- c(gender.cat.n,
+                   sum(mydata.black$R.gender == "Female", na.rm=TRUE))
> gender.cat.pct <- c(gender.cat.pct,
+                     round(100 * (sum(mydata.black$R.gender == "Female",
+                                      na.rm=TRUE) /
+                                  nrow(mydata.black)), 1))
> 
> category <- c(category, "Male")
> gender.cat.n <- c(gender.cat.n,
+                   sum(mydata.black$R.gender == "Male", na.rm=TRUE))
> gender.cat.pct <- c(gender.cat.pct,
+                     round(100 * (sum(mydata.black$R.gender == "Male",
+                                      na.rm=TRUE) /
+                                  nrow(mydata.black)), 1))
> 
> ## missing data
> category <- c(category, "Missing")
> gender.cat.n <- c(gender.cat.n,
+                   sum(is.na(mydata.black$R.gender)))
> gender.cat.pct <- c(gender.cat.pct,
+                     round(100 * (sum(is.na(mydata.black$R.gender)) /
+                                  nrow(mydata.black)), 1))
> 
> 
> black.gender.table <- data.frame(Gender=category,
+                                  N=gender.cat.n,
+                                  Pct=gender.cat.pct)
> 
> 
> 
> 
> 
> 
> 
> 
> ## gender among white respondents
> category <- NULL
> gender.cat.n <- NULL
> gender.cat.pct <- NULL
> category <- c(category, "Female")
> gender.cat.n <- c(gender.cat.n,
+                   sum(mydata.white$R.gender == "Female", na.rm=TRUE))
> gender.cat.pct <- c(gender.cat.pct,
+                     round(100 * (sum(mydata.white$R.gender == "Female",
+                                      na.rm=TRUE) /
+                                  nrow(mydata.white)), 1))
> 
> category <- c(category, "Male")
> gender.cat.n <- c(gender.cat.n,
+                   sum(mydata.white$R.gender == "Male", na.rm=TRUE))
> gender.cat.pct <- c(gender.cat.pct,
+                     round(100 * (sum(mydata.white$R.gender == "Male",
+                                      na.rm=TRUE) /
+                                  nrow(mydata.white)), 1))
> 
> ## missing data
> category <- c(category, "Missing")
> gender.cat.n <- c(gender.cat.n,
+                   sum(is.na(mydata.white$R.gender)))
> gender.cat.pct <- c(gender.cat.pct,
+                     round(100 * (sum(is.na(mydata.white$R.gender)) /
+                                  nrow(mydata.white)), 1))
> 
> 
> white.gender.table <- data.frame(Gender=category,
+                                  N=gender.cat.n,
+                                  Pct=gender.cat.pct)
> 
> 
> 
> 
> 
> 
> 
> ## education among black respondents
> educ.levels <- c("Did not finish high school",
+                  "High school graduate",
+                  "Some college",
+                  "College graduate (bachelor's degree)",
+                  "Graduate school")
> educ.cat.n <- NULL
> educ.cat.pct <- NULL
> for (educ in educ.levels){
+     educ.cat.n <- c(educ.cat.n, sum(mydata.black$R.edu == educ))
+     educ.cat.pct <- c(educ.cat.pct,
+                       round(100 * (sum(mydata.black$R.edu == educ,
+                                        na.rm=TRUE) /
+                                    nrow(mydata.black)), 1))    
+ }
> ## missing data
> educ.levels <- c(educ.levels, "Missing")
> educ.cat.n <- c(educ.cat.n,
+                   sum(is.na(mydata.black$R.edu)))
> educ.cat.pct <- c(educ.cat.pct,
+                     round(100 * (sum(is.na(mydata.black$R.edu)) /
+                                  nrow(mydata.black)), 1))
> 
> 
> black.educ.table <- data.frame(Education=educ.levels,
+                                N=educ.cat.n,
+                                Pct=educ.cat.pct)
> 
> 
> 
> 
> 
>     
> 
> 
> 
> ## education among white respondents
> educ.levels <- c("Did not finish high school",
+                  "High school graduate",
+                  "Some college",
+                  "College graduate (bachelor's degree)",
+                  "Graduate school")
> educ.cat.n <- NULL
> educ.cat.pct <- NULL
> for (educ in educ.levels){
+     educ.cat.n <- c(educ.cat.n, sum(mydata.white$R.edu == educ))
+     educ.cat.pct <- c(educ.cat.pct,
+                       round(100 * (sum(mydata.white$R.edu == educ,
+                                        na.rm=TRUE) /
+                                    nrow(mydata.white)), 1))    
+ }
> ## missing data
> educ.levels <- c(educ.levels, "Missing")
> educ.cat.n <- c(educ.cat.n,
+                   sum(is.na(mydata.white$R.edu)))
> educ.cat.pct <- c(educ.cat.pct,
+                     round(100 * (sum(is.na(mydata.white$R.edu)) /
+                                  nrow(mydata.white)), 1))
> 
> 
> white.educ.table <- data.frame(Education=educ.levels,
+                                N=educ.cat.n,
+                                Pct=educ.cat.pct)
> 
> 
> 
> 
> 
> library(xtable)
> 
>     
> sink("DemographicTables-Raw.txt")
> print(xtable(black.age.table, caption="Age Among Black Respondents", digits=1),
+       include.rownames=FALSE)
> print(xtable(white.age.table, caption="Age Among White Respondents", digits=1),
+       include.rownames=FALSE)
> 
> 
> print(xtable(black.gender.table, caption="Gender Among Black Respondents",
+              digits=1),
+       include.rownames=FALSE)
> print(xtable(white.gender.table, caption="Gender Among White Respondents",
+              digits=1),
+       include.rownames=FALSE)
> 
> print(xtable(black.educ.table, caption="Education Among Black Respondents",
+              digits=1),
+       include.rownames=FALSE)
> print(xtable(white.educ.table, caption="Education Among White Respondents",
+              digits=1),
+       include.rownames=FALSE)
> 
> 
> 
> 
> sink()
> 
> 
> proc.time()
   user  system elapsed 
  0.947   0.104   1.055 
